1 AOP Realization Redis cache
1.1 How to understand AOP
name : Section oriented programming
effect : Reduce the coupling of code in the system , And in the condition of not changing the original code, the function of the original method is extended .
The formula : AOP = Pointcut expression + Notification method
1.2 Notification type
1. Pre notice Before the target method is executed
2. The rear notice The target method is executed after execution
3. Abnormal notice When an exception is thrown during the execution of the target method
4. Final notice A notice to be executed at any time
characteristic : The four types of notification mentioned above Cannot interfere with the execution of the target method . It is generally used to record the running state of a program . monitor
5. Surrounding the notification A notification method that is executed before and after the target method is executed This method can control whether the target method runs or not .joinPoint.proceed(); Function as powerful .
1.3 Pointcut expression
understand : Pointcut expression is a judgment of whether a program enters the notification (IF)
effect : When the program is running , When the pointcut expression is satisfied, the notification method is executed , Realize business expansion .
species ( How to write it ):
- bean(bean The name of bean Of ID) Can only intercept a specific bean object Only one object can be matched
lg: bean(“itemServiceImpl”)
- within( Package name . Class name ) within(“com.jt.service.*”) Can match multiple objects
The principle of coarse-grained matching Match by class
`3. execution( return type Package name . Class name . Method name ( parameter list )) The most powerful use
lg : execution(* com.jt.service..*.*(..))
The return value type is arbitrary com.jt.service All methods of all classes under the package will be intercepted .
4.@annotation( Package name . Annotated name ) Match according to the annotation .`
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1.4 AOP Introductory cases
`package com.jt.aop;
import org.aspectj.lang.JoinPoint;
import org.aspectj.lang.ProceedingJoinPoint;
import org.aspectj.lang.annotation.*;
import org.springframework.stereotype.Component;
import org.springframework.stereotype.Service;
import java.util.Arrays;
@Aspect // I am a AOP Section class
@Component // Give the class to spring Container management
public class CacheAOP {
// The formula = Pointcut expression + Notification method
/**
* About the use of pointcut expressions
* coarse-grained :
* 1.bean(bean Of Id) One class
* 2.within( Package name . Class name ) Multiple classes
* fine-grained
*/
//@Pointcut("bean(itemCatServiceImpl)")
//@Pointcut("within(com.jt.service..*)") // Match multi level directory
@Pointcut("execution(* com.jt.service..*.*(..))") // Method parameter level
public void pointCut(){
// Define pointcut expression Just for space
}
// difference : pointCut() Represents a reference to a pointcut expression For multiple notifications Shared pointcuts
// @Before("bean(itemCatServiceImpl)") For a single notification . No need to reuse
// Define pre notice , Binding to pointcut expressions . Notice that the binding is the method
/**
* demand : Get information about the target object .
* 1. Get the path of the target method Package name . Class name . Method name
* 2. Gets the type of the target method class
* 3. Get the parameters passed
* 4. Record the current execution time
*/
@Before("pointCut()")
//@Before("bean(itemCatServiceImpl)")
public void before(JoinPoint joinPoint){
String className = joinPoint.getSignature().getDeclaringTypeName();
String methodName = joinPoint.getSignature().getName();
Class targetClass = joinPoint.getTarget().getClass();
Object[] args = joinPoint.getArgs();
Long runTime = System.currentTimeMillis();
System.out.println(" Method path :" +className+"."+methodName);
System.out.println(" Target object type :" + targetClass);
System.out.println(" Parameters :" + Arrays.toString(args));
System.out.println(" execution time :" + runTime+" millisecond ");
}
/* @AfterReturning("pointCut()")
public void afterReturn(){
System.out.println(" I'm a post notification ");
}
@After("pointCut()")
public void after(){
System.out.println(" I'm the final notice ");
}*/
/**
* The circular notification States
* matters needing attention :
* 1. Parameters must be added to the surround notification ProceedingJoinPoint
* 2.ProceedingJoinPoint Can only be used around notification
* 3.ProceedingJoinPoint If it's a parameter Must be in the first place of the parameter
*/
@Around("pointCut()")
public Object around(ProceedingJoinPoint joinPoint){
System.out.println(" Surround notification begins !!!");
Object result = null;
try {
result = joinPoint.proceed(); // Execute the next notification or target method
} catch (Throwable throwable) {
throwable.printStackTrace();
}
System.out.println(" End of surround notification ");
return result;
}
}`
2 About AOP Realization Redis cache
2.1 Custom cache annotations
problem : How to control Which methods need caching ? cacheFind()
Solution : In the form of custom annotations Define , If Method execution requires caching , Then mark the annotation .
Notes on notes :
1. Annotated name : cacheFind
2. Property parameters :
2.1 key: It should be added manually by the user himself Generally add business name After that, dynamic splicing forms the only key
2.2 seconds: The user can specify the timeout period of the data
`@Target(ElementType.METHOD) // Annotations work for methods
@Retention(RetentionPolicy.RUNTIME) // The operation period is valid
public @interface CacheFind {
public String preKey(); // User ID key The prefix of .
public int seconds() default 0; // If the user does not write, it means that there is no need to time out . If it is written, the user shall prevail .
}`
2.2 edit CacheAOP
`package com.jt.aop;
import com.jt.anno.CacheFind;
import com.jt.config.JedisConfig;
import com.jt.util.ObjectMapperUtil;
import org.aspectj.lang.JoinPoint;
import org.aspectj.lang.ProceedingJoinPoint;
import org.aspectj.lang.annotation.*;
import org.aspectj.lang.reflect.MethodSignature;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;
import org.springframework.stereotype.Service;
import redis.clients.jedis.Jedis;
import java.lang.reflect.Method;
import java.util.Arrays;
@Aspect // I am a AOP Section class
@Component // Give the class to spring Container management
public class CacheAOP {
@Autowired
private Jedis jedis;
/**
* section = The breakthrough point + Notification method
* Annotations associated + Surrounding the notification Control whether the target method is implemented
*
* difficulty :
* 1. How to get annotation objects
* 2. Dynamic generation key prekey + User parameter array
* 3. How to get the return value type of a method
*/
@Around("@annotation(cacheFind)")
public Object around(ProceedingJoinPoint joinPoint,CacheFind cacheFind){
Object result = null;
try {
//1. Splicing redis Storing data key
Object[] args = joinPoint.getArgs();
String key = cacheFind.preKey() +"::" + Arrays.toString(args);
//2. Inquire about redis Then judge whether there is data
if(jedis.exists(key)){
//redis There's a record in , There is no need to execute the target method
String json = jedis.get(key);
// Get the return value type of the method dynamically Look up Shape down
MethodSignature methodSignature = (MethodSignature) joinPoint.getSignature();
Class returnType = methodSignature.getReturnType();
result = ObjectMapperUtil.toObj(json,returnType);
System.out.println("AOP Inquire about redis cache ");
}else{
// Indicates that the data does not exist , Need to query database
result = joinPoint.proceed(); // Implementation target method and notification
// Save the query results to redis In the middle
String json = ObjectMapperUtil.toJSON(result);
// Determine whether the data needs a timeout
if(cacheFind.seconds()>0){
jedis.setex(key,cacheFind.seconds(),json);
}else {
jedis.set(key, json);
}
System.out.println("aop Execute target method query database ");
}
} catch (Throwable throwable) {
throwable.printStackTrace();
}
return result;
}
}`
3 About Redis Configuration instructions
3.1 About Redis Description of persistence
redis Data persistence is supported by default . When redis When there is data in, the data will be saved to disk regularly . When Redis When the server restarts The specified persistence file will be read according to the configuration file . Realize the recovery of memory data .
3.2 Persistence mode introduction
3.2.1 RDB Pattern
characteristic :
1.RDB The pattern is redis The default persistence policy for .
2.RDB The pattern records Redis Snapshot of memory data . The latest snapshot will cover the previous content all RDB Persistent files take up less space Persistence is more efficient .
3.RDB Patterns are persistent on a regular basis therefore It may lead to the loss of data .
command :
- save Requires immediate and immediate persistence Synchronous operation Other redis The operation will get stuck .
- bgsave Turn on background operation Asynchronous operations Because of asynchronous operation , So there's no guarantee rdb The files must be up to date and need to wait .
To configure :
1. Persistent file name :
2. Persistent file location
dir ./ The relative path
dir /usr/local/src/redis Absolute path 
3.RDB Pattern persistence strategy 
3.2.2 AOF Pattern
characteristic :
1.AOF Mode is off by default , It needs to be opened manually by the user 
- AOF Patterns are asynchronous operations It records the process of user's operation Sure Prevent users from losing data
- because AOF Patterns record the running state of a program So the persistence file is relatively large , It takes a long time to recover data . Need to optimize persistence files artificially
To configure :
3.2.2 Summary of persistence operations
1. If data loss is not allowed Use AOF The way
2. If we pursue efficiency Running a small amount of data loss use RDB Pattern
3. If we want to ensure efficiency And to ensure the data You should configure redis The cluster of Host use RDB The slave machine uses AOF
3.3 About Redis Memory strategy
3.3.1 An explanation of the memory policy
explain :Redis Data is stored in memory . If you always want to store data in memory It will inevitably lead to the overflow of memory data .
Solution :
- Keep as much as possible in redis Data addition timeout in .
- Using algorithms to optimize old data .
3.3.2 LRU Algorithm
characteristic : The best memory optimization algorithm to use .
LRU yes Least Recently Used Abbreviation , namely Recently at least use , Is a common page replacement algorithm , Select the most recent unused page to weed out . The algorithm gives each page an access field , Used to record the time of a page since it was last visited t, When a page has to be eliminated , Select one of the existing pages t The most valuable , That is, the most recently used pages are eliminated .
dimension : Time T
3.3.3 LFU Algorithm
LFU（least frequently used (LFU) page-replacement algorithm）. That is, the least frequent use of page replacement algorithm , It is required to replace the page with the lowest reference count at page replacement , Because frequently used pages should have a large number of references . But some pages are used a lot at the beginning , But it will not be used in the future , Such pages will stay in memory for a long time , So we can introduce Use the count register to shift right one bit at a time , The average number of uses to form an exponential decay .
dimension : Times of use
3.3.4 RANDOM Algorithm
Randomly delete data
3.3.5 TTL Algorithm
The algorithm to delete the data with time-out in advance .
3.3.6 Redis Memory data optimization
- volatile-lru The data with time-out is used lru Algorithm
2.allkeys-lru All the data use LRU Algorithm
3.volatile-lfu The data with time-out is used lfu Algorithm delete
4.allkeys-lfu All the data are based on lfu Algorithm delete
5.volatile-random The data of setting time-out time adopts random algorithm
6.allkeys-random Random algorithms for all data
7.volatile-ttl Set the timeout data TTL Algorithm
8.noeviction If memory overflows It will report an error and return . Do nothing . The default value is
4 About Redis Cache interview questions
Problem description : Because of the massive user requests If this time redis Server problem It may cause the whole system to crash .
Running speed :
- tomcat The server 150-250 Between JVM tuning 1000/ second
- NGINX 3-5 ten thousand / second
- REDIS read 11.2 ten thousand / second Write 8.6 ten thousand / second Average 10 ten thousand / second
4.1 Cache penetration
Problem description : Because of users High concurrency Visit in the environment Data that does not exist in the database , Easy to cause cache penetration .
How to solve : Set up IP Current limiting operation nginx in Or Microsoft Service Mechanism API Gateway implementation .
4.2 Cache breakdown
Problem description : Because of users High concurrency In the environment , Because some data existed in memory before , But for special reasons ( Data timeout / Data accidentally deleted ) Lead to redis Cache invalidation . So that a large number of users' requests directly access the database .
Common saying : Take advantage of his illness To kill him
How to solve :
1. When setting the timeout Don't set the same time .
2. Set multi level cache 
4.3 Cache avalanche
explain : because High concurrency Under the condition of Yes A lot of data failed . Lead to redis The hit rate is too low . It allows users to access the database directly ( The server ) Leading to a rout , It's called cache avalanche .
Solution :
1. Don't set the same timeout random number
2. Set multi level cache .
3. Improve redis Cache hit rate adjustment redis Memory optimization strategy use LRU And so on .